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Understanding Temporal Information Dynamics in Spiking Neural Networks
Pytorch code for [Understanding Temporal Information Dynamics in Spiking Neural Networks] - AAAI23
Dependencies
- Python 3.9
- PyTorch 1.10.0
- Spikingjelly
git clone https://github.com/fangwei123456/spikingjelly.git
cd spikingjelly
python setup.py install
Training and Computing fisher information
In this anonymous code, we provide a code for
(a) train_snn.py: train SNN from scratch
python train_snn.py --dataset 'cifar10' --arch 'resnet19' --optimizer 'sgd' --batch_size 128 --learning_rate 3e-1 --timestep 10
(b) train_snn_fisherinfo.py: computing fisher information from pretrained model
python train_snn_fisherinfo.py --dataset 'cifar10' --arch 'resnet19' --batch_size 16 --timestep 10
Also, for skipping (a) Train SNN from scratch, we provide pretrained parameters (link) for ResNet19_CIFAR10 from epoch 20, 120, 300.
Download three check point under snapshots/
folder